The present disclosure relates generally to a system and method for recommending educational resources. In particular, the present disclosure relates to applying clustering algorithms to student data for recommending educational resources.
Educational choices, such as grouping of students into classes, grouping students into groups for particular educational activities, selecting appropriate educational material, matching students to best suited educational aides, determining when and what educational interventions to use for students, etc., are based on information readily available to the educator making the decisions.
However, the information available is limited in at least two ways. The information is limited to the information available for the current students for whom the educational choices are being made. Similar decisions may have been made for other students having similar situations, with or without success, but that information is not available to the decision maker. Furthermore, the information about the current students may include assessment data, such as test scores or academic grades, which provide an overall indication of academic performance but may not indicate where specific weaknesses or knowledge deficits exist.
The process of making such educational decisions involving grouping students is complicated even with the limited amount of information, and would be all the more complicated with a large increase in information. Each time a decision is made the decision is static. Any change in constraints considered during the decision making process, such as due to a change in circumstances or the decision makers desire to consider different constraints, requires that the decision maker repeat the complicated decision making process.